Update app.py

app.py

@@ -104,12 +104,501 @@ if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
 
     
                
+st.subheader("Candidate Profile 2, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 2")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 2"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 3")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 4")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
+
+
+
+
+st.subheader("Candidate Profile 3, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 3")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 3"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 5")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 6")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
+st.subheader("Candidate Profile 4, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 4")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 4"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 7")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 8")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
 
 
 
+st.subheader("Candidate Profile 5, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 5")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 5"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 9")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 10")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
 
+st.subheader("Candidate Profile 6, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 6")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 6"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 11")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 12")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
 
+st.subheader("Candidate Profile 7, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 7")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 7"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 13")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 14")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
 
 
+st.subheader("Candidate Profile 8, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 8")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 8"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 16")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 18")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
 
+st.subheader("Candidate Profile 9, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 9")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 9"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 17")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 18")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+               
 
+st.subheader("Candidate Profile 10, divider = "green")
+             
+txt = st.text_area("Job description", key = "text 10")
+job = pd.Series(txt, name="Text")
+if 'upload_count' not in st.session_state:
+    st.session_state['upload_count'] = 0
+max_attempts = 2
+if st.session_state['upload_count'] < max_attempts:
+    uploaded_files = st.file_uploader(
+        "Upload your resume in .pdf format", type="pdf", key="candidate 10"
+    )
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                data = pd.Series(text_data, name = 'Text')
+                frames = [job, data]
+                result = pd.concat(frames)
+                
+                
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country","organization", "date", "time", "role", "skills", "year"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                
+                
+                fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+                fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig1, key = "figure 19")
+                
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                
+        
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 1', 'Jon Description'],
+                        y=['Resume 1', 'Job Description'])
+                st.plotly_chart(fig2, key = "figure 20")
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+else:
+    st.warning(f"You have reached the maximum upload attempts ({max_attempts})")
+if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
+    st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+    
+